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<b>Difusión pública de información sobre inteligencia artificial en las empresas</b>: diseño de indicadores de medición
Duas mãos se cumprimentando, sendo uma delas gerada por IA
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Palavras-chave

Divulgación de información
Inteligencia artificial
Aprendizaje automatic
Fiabilidad

Metrica

Como Citar

GUILLÉN PALOMINO, María Jesús; ARIAS ABELAIRA, Triana; RODRÍGUEZ ARIZA, Lázaro. Difusión pública de información sobre inteligencia artificial en las empresas: diseño de indicadores de medición. RDBCI: Revista Digital de Biblioteconomia e Ciência da Informação, Campinas, SP, v. 24, n. 00, p. e026007, 2026. DOI: 10.20396/rdbci.v24i00.8679450. Disponível em: https://periodicos.sbu.unicamp.br/ojs/index.php/rdbci/article/view/8679450. Acesso em: 7 maio. 2026.


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Resumo

Introducción: La difusión de información sobre inteligencia artificial se erige como un pilar fundamental para construir confianza y fomentar la adopción responsable de esta tecnología en el ámbito empresarial. Objetivo: El objetivo de este estudio consiste en el diseño de una herramienta que permita medir y comparar los comportamientos sobre difusión de información sobre inteligencia artificial que las empresas ofrecen a través de sus sitios web. Metodología: Para ello, se ha llevado a cabo un estudio exploratorio sobre todo tipo de documentos de acceso abierto publicados en el periodo 2021-2025, en el área de business economics, en la base de datos WOS sobre la información relativa a diferentes parámetros de la inteligência artificial. Resultados: Por tanto, se ha establecido un conjunto de cincuenta indicadores, distribuidos del siguiente modo entre los parámetros propuestos: siete para aprendizaje automático, nueve para empleo, ocho para fiabilidad, catorce para innovación, cinco para transparencia y siete para sostenibilidad. Conclusión: Se concluye que este estudio proporciona una valiosa herramienta para comprender y mejorar la forma en que las empresas comunican sobre la inteligencia artificial, lo que es esencial para construir confianza y promover su uso responsable.

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Referências

AECA – ASOCIACIÓN ESPAÑOLA DE CONTABILIDAD Y ADMINISTRACIÓN DE EMPRESAS. Un marco para la divulgación de responsabilidad digital corpotariva. Opinión Emitida no 4, Madrid, abr. 2024.

AECA – ASOCIACIÓN ESPAÑOLA DE CONTABILIDAD Y ADMINISTRACIÓN DE EMPRESAS. La responsabilidad digital corporativa. Opinión Emitida no 2, Madrid, nov. 2022.

AJAH, C. C. et al. The role of artificial intelligence and financial engineering for listed service companies in Nigeria. The Economics and Finance Letters, New York, NY, v. 11, n. 1, p. 1-17, 2024. DOI: https://doi.org/10.18488/29.v11i1.3596

AKDEMIR, D. M.; BULUT, Z. A. Business and customer-based chatbot activities: the role of customer satisfaction in online purchase intention and intention to reuse chatbots. Journal of Theoretical and Applied Electronic Commerce Research, Basel, v. 19, n. 4, p. 2961-2979, 2024. DOI: https://doi.org/10.3390/jtaer19040142

ALI, S. M. et al.Artificial intelligence approach to predict supply chain performance: implications for sustainability. Sustainability, Basel, v. 16, n. 6, p. 2373, 2024. DOI: https://doi.org/10.3390/su16062373

ALMARASHDA, H. A. H. A. et al. Human resource management and technology development in artificial intelligence adoption in the UAE energy sector. Journal of Applied Engineering Sciences, Oradea, v. 11, n. 2, p. 69-76, 2021. DOI: 10.2478/jaes-2021-0010

ALONSO ALMEIDA, M. M. La transparencia de las empresas en internet para la confianza de los accionistas e inversores: un análisis empírico. Cuadernos de Administración, Bogota, v. 22, n. 38, p. 11-30, 2009.

ALSHIBLY, H. H. et al. Examining the mediating role of customer empowerment: the impact of chatbot usability on customer satisfaction in Jordanian commercial banks. Cogent Business & Management, Abingdon, v. 11, n. 1, n. art. 2387196, 2024. DOI: https://doi.org/10.1080/23311975.2024.2387196

ALZUBAIDI, L. et al. Towards risk‐free trustworthy artificial intelligence: significance and requirements. International Journal of Intelligent Systems, Oxford, v. 2023, n. 1, n. art. 4459198, 2023. DOI: https://doi.org/10.1155/2023/4459198

ARIAS-ABELAIRA, T.; PACHE-DURAN, M.; RODRIGUEZ-ARIZA, L. Digital information dissemination: design of measurement indicators. Informação & Sociedade: Estudos, João Pessoa, v. 33, 2023.

ASHRAF, K. et al. Sales in commercial alleys and their association with air pollution: case study in South Korea. Sustainability, Basel, v. 16, n. 2, n. art. 530, 2024. DOI: https://doi.org/10.3390/su16020530

ASHRAFI, S. et al. Efficient resume based re-education for career recommendation in rapidly evolving job markets. IEEE Access, Piscataway, NJ, v. 11, p. 124350-124367, 2023. DOI: 10.1109/ACCESS.2023.3329576

AWAD, A. Artificial intelligence and marketing innovation: the mediating role of organizational culture. Innovative Marketing, Ukraine, v. 20, n. 3, p. 170-181, 2024. DOI: 10.21511/im.20(3).2024.14

AYDIN, E.; TURAN, M. An AI-based shortlisting model for sustainability of human resource management. Sustainability, Basel, v. 15, n. 3, n. art. 2737, 2023. DOI: https://doi.org/10.3390/su15032737

BAHOO, S.; CUCCULELLI, M.; QAMAR, D. Artificial intelligence and corporate innovation: a review and research agenda. Technological Forecasting and Social Change, New York, NY, v. 188, n. art. 122264, 2023. DOI: https://doi.org/10.1016/j.techfore.2022.122264

BALCIOĞLU, Y. S.; ÇELIK, A. A.; ALTINDAĞ, E. Artificial intelligence integration in Sustainable Business Practices: a text mining analysis of USA firms. Sustainability, Basel, v. 16, n. 15, n. art. 6334, 2024. DOI: https://doi.org/10.3390/su16156334

BANKINS, S.; FORMOSA, P. The ethical implications of artificial intelligence (AI) for meaningful work. Journal of Business Ethics, Dordrecht, v. 185, n. 4, p. 725-740, 2023. DOI: https://doi.org/10.1007/s10551-023-05339-7

BÉCUE, A.; GAMA, J.; BRITO, P. Q. AI’s effect on innovation capacity in the context of industry 5.0: a scoping review. Artificial Intelligence Review, Dordrecht, v. 57, n. 8, n. art. 215, 2024. DOI: https://doi.org/10.1007/s10462-024-10864-6

BELTER, J.; HERING, M.; WEICHBROTH, P. Motion trajectory prediction in warehouse management systems: a systematic literature review. Applied Sciences, Bucharest, v. 13, n. 17, n. art. 9780, 2023. DOI: https://doi.org/10.3390/app13179780

BEULEN, E.; PLUGGE, A.; VAN HILLEGERSBERG, J. Formal and relational governance of artificial intelligence outsourcing. Information Systems and e-Business Management, Heidelberg, v. 20, n. 4, p. 719-748, 2022. DOI: https://doi.org/10.1007/s10257-022-00562-7

BICKLEY, S. J.; MACINTYRE, A.; TORGLER, B. Artificial intelligence and big data in sustainable entrepreneurship. Journal of Economic Surveys, United Kingdom, v. 39, n. 1, p. 103-145, fev. 2025 DOI: https://doi.org/10.1111/joes.12611

BIRIM, S. et al. The derived demand for advertising expenses and implications on sustainability: a comparative study using deep learning and traditional machine learning methods. Annals of Operations Research, New York, NY, v. 339, n. 1, p. 131-161, 2024. DOI: https://doi.org/10.1007/s10479-021-04429-x

BOLESNIKOV, M. et al. Perception of innovative usage of ai in optimizing customer purchasing experience within the sustainable fashion industry. Sustainability, Basel, v. 14, n. 16, n. art. 10082, 2022. DOI: https://doi.org/10.3390/su141610082

BOLOȘ, M.I. et al. AI chatbots: fast tracking sustainability report analysis for enhanced decision making. Amfiteatru Economic, Bucharest, v. 26, n. 18, n. art. 1241, 2024. DOI: https://doi.org/10.24818/EA/2024/S18/1241

BOLTON, G. E.; KATOK, E.; OCKENFELS, A. How effective are electronic reputation mechanisms? An experimental investigation. Management science, Catonsville, MD, v. 50, n. 11, p. 1587-1602, 2004.

BONSÓN, E.; ESCOBAR, T. La difusión voluntaria de información financiera en Internet. Un análisis comparativo entre Estados Unidos, Europa del Este y la Unión Europea. Revista Española de Financiación y Contabilidad, Abingdon, v. 33, n. 123, p. 1063-1101, 2004.

BOOYSE, D.; SCHEEPERS, C. B. Barriers to adopting automated organisational decision-making through the use of artificial intelligence. Management Research Review, Bingley, v. 47, n. 1, p. 64-85, 2024. DOI: https://doi.org/10.1108/MRR-09-2021-0701

BRAGANZA, A. et al. Gigification, job engagement and satisfaction: the moderating role of AI enabled system automation in operations management. Production Planning & Control, Abingdon, v. 33, n. 16, p. 1534-1547, 2022. DOI: https://doi.org/10.1080/09537287.2021.1882692

BRIOZZO, A. E. Divulgación de información sobre factores ambientales, sociales y de gobernanza-ASG-en emisoras de bonos verdes, sociales y sustentables. Revista de Investigación en Modelos Financieros, Buenos Aires, v. 1, p. 49-65, 2024.

BUDHWAR, P. et al. Human resource management in the age of generative artificial intelligence: perspectives and research directions on chatGPT. Human Resource Management Journal, Chichester, v. 33, n. 3, p. 606-659, 2023. DOI:

https://doi.org/10.1111/1748-8583.12524

BUHMANN, A.; FIESELER, C. Deep learning meets deep democracy: deliberative governance and responsible innovation in artificial intelligence. Business Ethics Quarterly, New York, NY, v. 33, n. 1, p. 146-179, 2023.

BUSTAMANTE, R. P.; PÉREZ, X. M. C.; DEL PILAR ESCOTT-MOTA, M. Radical change and dominant character of digital transformation in artificial intelligence entrepreneurship in less innovative economies. Journal of the Knowledge Economy, New York, NY, v. 15, p. 19490-19516, 2024. DOI: https://doi.org/10.1007/s13132-024-01807-1

CAMILLERI, M. A. Artificial intelligence governance: ethical considerations and implications for social responsibility. Expert Systems, Chichester, v. 41, n. 7, n. art. e13406, 2024. DOI:

https://doi.org/10.1111/exsy.13406

CAMPBELL, D.; SHRIVES, P.; BOHMBACH‐SAAGER, H. Voluntary disclosure of mission statements in corporate annual reports: signaling what and to whom? Business and Society Review, Hoboken, NJ, v. 106, n. 1, p. 65-87, 2001. DOI: https://doi.org/10.1111/0045-3609.00102

CAMPBELL, M.; JOVANOVIĆ, M. Disinfecting AI: mitigating generative AI’s top risks. Computer, Futyu-shi, Tokyo, v. 57, n. 5, p. 111-116, 2024. DOI: 10.1109/MC.2024.3374433

CHEN, J. [Retracted] Application analysis of artificial intelligence algorithm in accounting field under the background of innovation economy. Mobile Information Systems, London, v. 2022, n. 1, n. art. 7970237, 2022. https://doi.org/10.1155/2022/7970237

CHEN, K. et al. Does artificial intelligence promote common prosperity within enterprises? Evidence from Chinese-listed companies in the service industry. Technological Forecasting and Social Change, New York, NY, v. 200, n. art. 123180, 2024. DOI: https://doi.org/10.1016/j.techfore.2023.123180

CHEN, X.; YE, S.; HUANG, C. [Retracted] Cluster‐based mutual fund classification and price prediction using machine learning for robo‐advisors. Computational Intelligence and Neuroscience, London, v. 2021, n. 1, n. art. 4984265, 2021. DOI: https://doi.org/10.1155/2021/4984265

CHEN, Z. Collaboration among recruiters and artificial intelligence: removing human prejudices in employment. Cognition, Technology & Work, London, v. 25, n. 1, p. 135-149, 2023. DOI: https://doi.org/10.1007/s10111-022-00716-0

CHOWDHURY, S. et al. Embedding transparency in artificial intelligence machine learning models: managerial implications on predicting and explaining employee turnover. The International Journal of Human Resource Management, Abingdon, v. 34, n. 14, p. 2732-2764, 2023. DOI: https://doi.org/10.1080/09585192.2022.2066981

COUSSEMENT, K. et al. Explainable AI for enhanced decision-making. Decision Support Systems, Amsterdam, v. 184, n. art. 114276, 2024. DOI: https://doi.org/10.1016/j.dss.2024.114276

CUI, X.; XU, B.; RAZZAQ, A. Can application of artificial intelligence in enterprises promote the corporate governance? Frontiers in Environmental Science, Lausanne, v. 10, n. art. 944467, 2022. DOI: https://doi.org/10.3389/fenvs.2022.944467

DAHAL, K. R. et al. A comparative study on effect of news sentiment on stock price prediction with deep learning architecture. PLoS One, San Francisco, v. 18, n. 4, n. art. e0284695, 2023.

DOI: https://doi.org/10.1371/journal.pone.0284695

D’ALMEIDA, A. L. et al. Digital transformation: a review on artificial intelligence techniques in drilling and production applications. International Journal of Advanced Manufacturing Technology, London, v. 119, n. 9, p. 5553-5582, 2022. DOI: https://doi.org/10.1007/s00170-021-08631-w

DANCAUSA MILLÁN, M. G.; MILLÁN VÁZQUEZ DE LA TORRE, M. G. An economic perspective on the implementation of artificial intelligence in the restaurant sector. Administrative Sciences, Basel, v. 14, n. 9, n. art. 214, 2024. DOI: https://doi.org/10.3390/admsci14090214

DARGAN, S. et al. A survey of deep learning and its applications: a new paradigm to machine learning. Archives of Computational Methods in Engineering, Dordrecht, v. 27, p. 1071-1092, 2020. DOI: https://doi.org/10.1007/s11831-019-09344-w

DIRECTIVA EUROPEA. Directiva 2022/2464 del Parlamento Europeo y del Consejo de 14 de diciembre de 2022 por la que se modifican el Reglamento (UE) no 537/2014, la Directiva 2004/109/CE, la Directiva 2006/43/CE y la Directiva 2013/34/UE, por lo que respecta a la presentación de información sobre sostenibilidad por parte de las empresas. Diario Oficial de la Unión Europea, Bruxelas, 2022.

DIRECTIVA EUROPEA. Directiva 2014/95/UE del Parlamento Europeo y del Consejo de 22 de octubre de 2014 – por la que se modifica la Directiva 2013/34/UE en lo que respecta a la divulgación de información no financiera e información sobre diversidad por parte de determinadas grandes empresas y determinados grupos. Diario Oficial de la Unión Europea, Bruxelas, 2014. Disponível em: https://www.boe.es/doue/2014/330/L00001-00009.pdf. Acesso em: 23 abr. 2025.

DOMÍNGUEZ, L. R.; ÁLVAREZ, I. G.; SÁNCHEZ, I. M. G. Determinantes de la divulgación voluntaria de información estratégica en internet: un estudio de las empresas cotizadas. Revista Europea de Dirección y Economía de la Empresa, Bingley, v. 19, n. 1, p. 9-26, 2010.

DRYDAKIS, N. Artificial intelligence capital and employment prospects. Oxford Economic Papers, Oxford, v. 76, n. 4, p. 901-919, 2024. DOI: https://doi.org/10.1093/oep/gpae005

EACHEMPATI, P. et al. Validating the impact of accounting disclosures on stock market: a deep neural network approach. Technological Forecasting and Social Change, New York, NY, v. 170, n. art. 120903, 2021. DOI: https://doi.org/10.1016/j.techfore.2021.120903

ENHOLM, I. M. et al. Artificial intelligence and business value: a literature review. Information Systems Frontiers, New York, NY, v. 24, n. 5, p. 1709-1734, 2022. DOI: https://doi.org/10.1007/s10796-021-10186-w

ESPANHA. Ministerio para la Transformación Digital y de la Función Pública. Estrategia de Inteligencia Artificial 2024. Madrid, 2024.

ESPANHA. Ministerio de Asuntos Económicos y Transformación Digital. Anteproyecto de Ley xx/202X, de xx de xxxxxx, por la que se regula el marco de información corporativa obre cuestiones medioambientales, sociales y de gobernanza. Madrid, 2022.

FAN, C. et al. [Retracted] Research on the correlation between information and communication technology development and consumer spending based on artificial intelligence and time series econometric model. Journal of Electrical and Computer Engineering, Oxford, v. 2022, n. 6, n. art. 1645232, 2022. DOI: 10.1155/2022/1645232

FAN, X. et al. The impact of improving employee psychological empowerment and job performance based on deep learning and artificial intelligence. Journal of Organizational and end User Computing, Hershey, PA, v. 35, n. 3, p. 1–14, 2023. DOI: 10.4018/JOEUC.321639

FELFERNIG, A. et al. Recommender systems for sustainability: overview and research issues. Frontiers in Big Data, Lausanne, v. 6, n. art. 1284511, 2023. DOI: https://doi.org/10.3389/fdata.2023.1284511

FONSECA, L. et al. Leveraging ChatGPT for sustainability: a framework for SMEs to align with UN sustainable development goals and tackle sustainable development challenges. Management & Marketing, v. 19, n. 3, p. 471-497, 2024. DOI: https://doi.org/10.2478/mmcks-2024-0021

GANDÍA, J. A. G. et al. Towards sustainable business in the automation era: Exploring its transformative impact from top management and employee perspective. Technological Forecasting and Social Change, New York, NY, v. 210, n. art. 123908, 2025. DOI: https://doi.org/10.1016/j.techfore.2024.123908

GAO, L.; LIU, Z. Unraveling the multifaceted nexus of artificial intelligence sports and user willingness: a focus on technology readiness, perceived usefulness, and green consciousness. Sustainability, Basel, v. 15, n. 18, n. art. 13961, 2023. DOI: https://doi.org/10.3390/su151813961

GARCÍA MECA, E.; MARTÍNEZ CONESA, I. Divulgación voluntaria de información empresarial: índices de revelación. Partida Doble, Planta, Madrid, v. 157, p. 66-77, 2004.

GARCÍA-BENAU, M. A.; BOLLAS-ARAYA, H. M.; SIERRA-GARCÍA, L. Non-financial reporting in Spain. The effects of the adoption of the 2014 EU Directive: la información no financiera en España. Los efectos de la adopción de la Directiva de la UE de 2014. Revista de Contabilidad-Spanish Accounting Review, [S. l.], v. 25, n. 1, p. 3-15, 2022. DOI: 10.6018/rcsar.392631

GEORGIEFF, A.; HYEE, R. Artificial intelligence and employment: new cross-country evidence. Frontiers in artificial intelligence, Lausanne, v. 5, n. art. 832736, 2022. DOI: https://doi.org/10.3389/frai.2022.832736

GHOLAMI, S. et al. Using deep learning to enhance business intelligence in organizational management. Data Science in Finance and Economics, Springfield, MO, v. 3, n. 4, p. 337-353, 2023. DOI: 10.3934/DSFE.2023020

GIORDANO, V. et al. The impact of ChatGPT on human skills: a quantitative study on twitter data. Technological Forecasting and Social Change, New York, NY, v. 203, n. art. 123389, 2024. DOI: https://doi.org/10.1016/j.techfore.2024.123389

GONG, R. et al. A bibliometric analysis of green supply chain management based on the Web of Science (WOS) platform. Sustainability, Basel, v. 11, n. 12, n. art. 3459, 2019. DOI: https://doi.org/10.3390/su11123459

GOVINDAN, K. How artificial intelligence drives sustainable frugal innovation: a multitheoretical perspective. IEEE Transactions on Engineering Management, Piscataway, NJ, v. 71, p. 638-655, 2022. DOI: 10.1109/TEM.2021.3116187

GRĂDINARU, G. I. et al. The development of educational competences for Romanian students in the context of the evolution of data science and artificial intelligence. Amfiteatru Economic, Bucharest, v. 26, n. 65, p. 14-32, 2024. DOI: 10.24818/EA/2024/65/14

GRANDI, F. et al. A methodology to guide companies in using explainable AI-driven interfaces in manufacturing contexts. Procedia Computer Science, Amsterdam, v. 232, p. 3112-3120, 2024. DOI: https://doi.org/10.1016/j.procs.2024.02.127

GRAY, S. J.; RADEBAUGH, L. H.; ROBERTS, C. B. International perceptions of cost constraints on voluntary information disclosures: a comparative study of UK and US multinationals. Journal of International Business Studies, London, v. 21, p. 597-622, 1990. DOI: https://doi.org/10.1057/palgrave.jibs.8490343

GUDIGANTALA, N.; MADHAVARAM, S.; BICEN, P. An AI decision‐making framework for business value maximization. AI Magazine, Hoboken, NJ, v. 44, n. 1, p. 67-84, 2023. DOI: https://doi.org/10.1002/aaai.12076

HAEFNER, N. et al. Artificial intelligence and innovation management: a review, framework, and research agenda. Technological Forecasting and Social Change, New York, NY, v. 162, n. art. 120392, 2021. DOI: https://doi.org/10.1016/j.techfore.2020.120392

HAJEK, P.; NOVOTNY, J. Fuzzy rule-based prediction of gold prices using news affect. Expert Systems with Applications, Oxford, v. 193, n. art. 116487, 2022. DOI: https://doi.org/10.1016/j.eswa.2021.116487

HAJNIĆ, M.; BOSHKOSKA, B. M. A disruptive decision support platform for reengineering the strategic transfer of employees. IEEE Access, Piscataway, NJ, v. 9, p. 29921-29928, 2021. DOI: 10.1109/ACCESS.2021.3059895

HENRIQUES, H.; PEREIRA, L. N. Hotel demand forecasting models and methods using artificial intelligence: a systematic literature review. Tourism & Management Studies, Portugal, v. 20, n. 3, p. 39-51, 2024. DOI: https://doi.org/10.18089/tms.20240304

HOLMSTRÖM, J.; HÄLLGREN, M. AI management beyond the hype: exploring the co-constitution of AI and organizational context. AI & Society, London, v. 37, p. 1575-1585, 2022. DOI: https://doi.org/10.1007/s00146-021-01249-2

HU, Z.; ZHAO, Y.; KHUSHI, M. A survey of forex and stock price prediction using deep learning. Applied System Innovation, Basel, v. 4, n. 1, n. art. 9, 2021. DOI: https://doi.org/10.3390/asi4010009

HUSSAIN, M. When, where, and which?: navigating the intersection of computer vision and generative ai for strategic business integration. IEEE Access, Piscataway, NJ, v. 11, p. 127202-127215, 2023. DOI: https://doi.org/10.1109/ACCESS.2023.3332468

ISLAM, T. et al. Transforming digital marketing with generative AI. Computers, Ottawa, ON, v. 13, n. 7, n. art. 168, 2024. DOI: https://doi.org/10.3390/computers13070168

JAN, Z. et al. Artificial intelligence for industry 4.0: systematic review of applications, challenges, and opportunities. Expert Systems with Applications, Oxford, v. 216, n. art. 119456, 2023. DOI: https://doi.org/10.1016/j.eswa.2022.119456

JANIESCH, C.; ZSCHECH, P.; HEINRICH, K. Machine learning and deep learning. Electronic Markets, Heidelberg, v. 31, n. 3, p. 685-695, 2021. DOI: https://doi.org/10.1007/s12525-021-00475-2

JAZDAUSKAITE, J. et al. Evaluation of the impact of science and technology on the labour market. Marketing i Menedžment Innovacij, [S. l.], n. 4, p. 153-167, 2021.

JIA, L. Construction and empirical analysis of college students’ job satisfaction index model using artificial intelligence. International Journal of Technology and Human Interaction, Hershey, PA, v. 18, n. 2, p. 1-21, 2022. DOI: https://doi.org/10.4018/IJTHI.313603

JORZIK, P. et al. Artificial intelligence-enabled business model innovation: competencies and roles of top management. IEEE Transactions on Engineering Management, Piscataway, NJ, v. 71, p. 7044-7056, 2023. DOI: 10.1109/TEM.2023.3275643

JORZIK, P. et al.Sowing the seeds for sustainability: a business model innovation perspective on artificial intelligence in green technology startups. Technological Forecasting and Social Change, New York, NY, v. 208, n. art. 123653, 2024. DOI: https://doi.org/10.1016/j.techfore.2024.123653

KAZANCOGLU, I. et al. Using emerging technologies to improve the sustainability and resilience of supply chains in a fuzzy environment in the context of COVID-19. Annals of Operations Research, New York, NY, v. 322, n. 1, p. 217-240, 2023. DOI: https://doi.org/10.1007/s10479-022-04775-4

KHALIFA, N.; ABD ELGHANY, M.; ABD ELGHANY, M. Exploratory research on digitalization transformation practices within supply chain management context in developing countries specifically Egypt in the MENA region. Cogent Business & Management, Abingdon, v. 8, n. 1, n. art. 1965459, 2021. DOI: https://doi.org/10.1080/23311975.2021.1965459

KHNEYZER, C.; BOUSTANY, Z.; DAGHER, J. AI-driven chatbots in CRM: economic and managerial implications across industries. Administrative Sciences, Basel, v. 14, n. 8, n. art. 182, 2024. DOI: https://doi.org/10.3390/admsci14080182

KHOGALI, H. O.; MEKID, S. The blended future of automation and AI: examining some long-term societal and ethical impact features. Technology in Society, Oxford, v. 73, n. art. 102232, 2023. DOI: https://doi.org/10.1016/j.techsoc.2023.102232

KOLAGAR, M.; PARIDA, V.; SJÖDIN, D. Linking digital servitization and industrial sustainability performance: a configurational perspective on smart solution strategies. IEEE Transactions on Engineering Management, Piscataway, NJ, v. 71, p. 7743-7755, 2024. DOI: 10.1109/TEM.2024.3383462

KUMARAPPAN, J. et al. Federated learning enhanced MLP–LSTM modeling in an integrated deep learning pipeline for stock market prediction. International Journal of Computational Intelligence Systems, Basel, v. 17, n. 1, n. art. 267, 2024. DOI: https://doi.org/10.1007/s44196-024-00680-9

KUZIOR, A.; SIRA, M.; BROŻEK, P. Use of artificial intelligence in terms of open innovation process and management. Sustainability, Basel, v. 15, n. 9, n. art. 7205, 2023. DOI: https://doi.org/10.3390/su15097205

LATORRE-BIEL, J. I. et al. Combining simheuristics with Petri nets for solving the stochastic vehicle routing problem with correlated demands. Expert Systems with Applications, Oxford, v. 168, n. art. 114240, 2021. DOI: https://doi.org/10.1016/j.eswa.2020.114240

LAZAROIU, G. et al. Digital twin-based cyber-physical manufacturing systems, extended reality metaverse enterprise and production management algorithms, and internet of things financial and labor market technologies in generative artificial intelligence economics. Oeconomia Copernicana, Torun, v. 15, n. 3, p. 837-870, 2024. DOI: 10.24136/oc.3183

LEE, D. et al. Discovering sustainable business partnerships through a deep learning approach to maximize potential value. Sustainability, Basel, v. 15, n. 22, n. art. 15885, 2023. DOI: https://doi.org/10.3390/su152215885

LI, D.; WANG, H.; WANG, J. Artificial intelligence and technological innovation: evidence from China’s strategic emerging industries. Sustainability, Basel, v. 16, n. 16, n. art. 7226, 2024. DOI: https://doi.org/10.3390/su16167226

LI, Y.; LIU, P.; WANG, Z. Stock trading strategies based on deep reinforcement learning. Scientific Programming, London, v. 2022, n. 1, n. art. 4698656, 2022. DOI:

https://doi.org/10.1155/2022/4698656

LI, Y. Application analysis of artificial intelligent neural network based on intelligent diagnosis. Procedia Computer Science, Amsterdam, v. 208, p. 31-35, 2022. DOI: https://doi.org/10.1016/j.procs.2022.10.006

LIU, J. et al. Impact of artificial intelligence on manufacturing industry global value chain position. Sustainability, Basel, v. 16, n. 3, n. art. 1341, 2024. DOI: https://doi.org/10.3390/su16031341

LIU, Q. Analysis of collaborative driving effect of artificial intelligence on knowledge innovation management. Scientific Programming, London, v. 2022, n. 1, n. art. 8223724, 2022. DOI: https://doi.org/10.1155/2022/8223724

LIU, Y. et al. Revolutionising financial portfolio management: the non-stationary transformer’s fusion of macroeconomic indicators and sentiment analysis in a deep reinforcement learning framework. Applied Sciences, Bucharest, v. 14, n. 1, n. art. 274, 2023. DOI: https://doi.org/10.3390/app14010274

LOMBARDO, G. et al. Machine learning for bankruptcy prediction in the American stock market: dataset and benchmarks. Future Internet, Basel, v. 14, n. 8, n. art. 244, 2022. DOI: https://doi.org/10.3390/fi14080244

MANNING, L. et al. Artificial intelligence and ethics within the food sector: developing a common language for technology adoption across the supply chain. Trends in Food Science & Technology, Oxford, v. 125, p. 33-42, 2022. DOI: https://doi.org/10.1016/j.tifs.2022.04.025

MANTA, A. G. et al. Industry 4.0 transformation: analysing the impact of artificial intelligence on the banking sector through bibliometric trends. Electronics, Basel, v. 13, n. 9, n. art. 1693, 2024. DOI: https://doi.org/10.3390/electronics13091693

MARTÍN ZAMORA, M. P. et al. La divulgación de información no financiera en España. Gestión, Revista de Economía, Murcia, n. 69, p. 15-22, 2019.

MARTÍNEZ, M. A. D. et al. Artificial intelligence an essential factor for the benefit of companies: systematic review of the literature. Cogent Engineering, Abingdon, v. 11, n. 1, n. art. 2380344, 2024. DOI: https://doi.org/10.1080/23311916.2024.2380344

MARTÍNEZ‐FERRERO, J.; RUIZ‐CANO, D.; GARCÍA‐SÁNCHEZ, I. M. The causal link between sustainable disclosure and information asymmetry: the moderating role of the stakeholder protection context. Corporate Social Responsibility and Environmental Management, Oxford, v. 23, n. 5, p. 319-332, 2016. DOI: https://doi.org/10.1002/csr.1379

MASOODIFAR, M.; ARSLAN, İ. K.; TEKEOĞLU, A. N. T. Artificial intelligence in global business and its communication. Journal of International Trade, Logistics and Law, Istanbul, v. 9, n. 1, p. 278-284, 2023.

MEROLA, R. Inclusive growth in the era of automation and AI: how can taxation help? Frontiers in artificial intelligence, Lausanne, v. 5, n. art. 867832, 2022. DOI: https://doi.org/10.3389/frai.2022.867832

MODGIL, S.; SINGH, R. K.; HANNIBAL, C. Artificial intelligence for supply chain resilience: learning from Covid-19. The International Journal of Logistics Management, Bingley, v. 33, n. 4, p. 1246-1268, 2022. DOI: https://doi.org/10.1108/IJLM-02-2021-0094

MONIZ, A. B.; CANDEIAS, M.; BOAVIDA, N. Changes in productivity and labour relations: artificial intelligence in the automotive sector in Portugal. International Journal of Automotive Technology and Management, Geneva, v. 22, n. 2, p. 222-244, 2022. DOI: 10.1504/IJATM.2022.124366

MORALES PARADA, F. A.; JARNE JARNE, J. I. Divulgación de informaciones corporativas en las website de empresas cotizadas mexicanas: estado de situación y evolución. Trascender, Contabilidad y Gestión, México, v. 7, n. 21, p. 69-89, 2022. DOI: https://doi.org/10.36791/tcg.v7i21sept-dic.182.

МОХАММЕД, А. М.; ВАХХАБ, А. The relationship between artificial intelligence and e-accounting programs: impact on the quality of financial reports in iraqi banks. Financial and Credit Activity: Problems of Theory and Practice, Ucrânia, v. 6, n. 59, p. 180-193, 2024. DOI: https://doi.org/10.55643/fcaptp.6.59.2024.4522

MUÑOZ PAREDES, J. M. Nuevas tecnologías en el funcionamiento de las juntas generales y de los consejos de administración. Madrid: Civitas Ediciones, 2005.

NAEEM, R.; KOHTAMÄKI, M.; PARIDA, V. Artificial intelligence enabled product: service innovation: past achievements and future directions. Review of Managerial Science, Heidelberg, v. 19, p. 2149-2192, 2025. DOI: https://doi.org/10.1007/s11846-024-00757-x.

NEVADO GIL, M.; GALLARDO VÁZQUEZ, D.; SÁNCHEZ HERNÁNDEZ, M. La administración local y su implicación en la creación de una cultura socialmente responsable. Prisma Social, Las Matas, n. 10, p. 64-118, 2013.

OGBEIBU, S. et al. Green talent management and turnover intention: the roles of leader STARA competence and digital task interdependence. Journal of Intellectual Capital, Bingley, v. 23, n. 1, p. 27-55, 2022. DOI: https://doi.org/10.1108/JIC-01-2021-0016

OLAN, F. et al. Sustainable supply chain finance and supply networks: the role of artificial intelligence. IEEE Transactions on Engineering Management, Piscataway, NJ, v. 71, p. 13296-13311, 2022. DOI: 10.1109/TEM.2021.3133104

ONCIOIU, I. et al. The influence of social networks on the digital recruitment of human resources: an empirical study in the tourism sector. Sustainability, Basel, v. 14, n. 6, n. art. 3693, 2022. DOI: https://doi.org/10.3390/su14063693

ONYEAKA, H. et al. Using artificial intelligence to tackle food waste and enhance the circular economy: maximising resource efficiency and Minimising environmental impact: a review. Sustainability, Basel, v. 15, n. 13, n. art. 10482, 2023. DOI: https://doi.org/10.3390/su151310482

ORTIZ, E.; CLAVEL, J. G. Índices de revelación de información: una propuesta de mejora de la metodología. Aplicación a la información sobre recursos humanos incluida en los informes 20F. Revista Española de Financiación y Contabilidad, Abingdon, v. 35, n. 128, p. 87-113, 2006.

PACHE DURÁN, M. et al. Divulgación de información sobre responsabilidad social en la administración pública a través de las páginas web: un estudio aplicado a los gobiernos provinciales de Angola. In: ENCUENTRO INTERNACIONAL AECA, 20., 2022, Oporto, Portugal. Anais... Madrid: AECA, 2022.

PAI, R. Y. et al. Integrating artificial intelligence for knowledge management systems–synergy among people and technology: a systematic review of the evidence. Economic Research-Ekonomska Istraživanja, London, v. 35, n. 1, p. 7043-7065, 2022. DOI: https://doi.org/10.1080/1331677X.2022.2058976

PARLAMENTO EUROPEO; CONSEJO DE LA UNIÓN EUROPEA. Reglamento (UE) 2024/1689 del Parlamento Europeo y del Consejo, de 13 de junio de 2024, por el que se establecen normas armonizadas en materia de inteligencia artificial y por el que se modifican los Reglamentos (CE) n. 300/2008, (UE) n. 167/2013, (UE) n. 168/2013, (UE) 2018/858, (UE) 2018/1139 y (UE) 2019/2144 y las Directivas 2014/90/UE, (UE) 2016/797 y (UE) 2020/1828 (Reglamento de Inteligencia Artificial) Texto pertinente a efectos del EEE. [S.l.], 2024. Disponível em: http://data.europa.eu/eli/reg/2024/1689/oj.

PENG, Y. et al. Riding the waves of artificial intelligence in advancing accounting and its implications for sustainable development goals. Sustainability, Basel, v. 15, n. 19, n. art. 14165, 2023. DOI: https://doi.org/10.3390/su151914165

PISONI, G.; MOLNÁR, B. AI-based solution for sustainability tracing for companies. International Journal of Knowledge Management, Hershey, PA, v. 20, n. 1, p. 1-17, 2024. DOI: 10.4018/IJKM.340723

RAJAGOPAL, N. K. et al. Future of business culture: an artificial intelligence‐driven digital framework for organization decision‐making process. Complexity, Oxford, v. 2022, n. 1, n. art. 7796507, 2022. DOI: https://doi.org/10.1155/2022/7796507

RAJESH, A. S.; PRABHUSWAMY, M. S.; SATISH, H. S. Smart manufacturing through machine learning: a review, perspective and future directions to machining industry. Journal of Engineering, Oxford, v. 2022, n. art. 9735862, 2022. DOI: 10.1155/2022/9735862

REMONDINO, M.; ZANIN, A. Logistics and agri-food: digitization to increase competitive advantage and sustainability. Literature review and the case of Italy. Sustainability, Basel, v. 14, n. 2, n. art. 787, 2022. DOI: https://doi.org/10.3390/su14020787

ROBERT, L. P. et al. Designing fair AI for managing employees in organizations: a review, critique, and design agenda. Human–Computer Interaction, New York, NY, v. 35, n. 5-6, p. 545-575, 2020. DOI: https://doi.org/10.1080/07370024.2020.1735391

ROBERTS, D. L.; CANDI, M. Artificial intelligence and innovation management: charting the evolving landscape. Technovation, Oxford, v. 136, n. art. 103081, 2024. DOI: https://doi.org/10.1016/j.technovation.2024.103081

ROUMELIOTIS, K. I.; TSELIKAS, N. D.; NASIOPOULOS, D. K. Unveiling sustainability in ecommerce: GPT-Powered software for identifying sustainable product features. Sustainability, Basel, v. 15, n. 15, n. art. 12015, 2023. DOI: https://doi.org/10.3390/su151512015

ROWAN, Neil J. et al. Digital transformation of peatland eco-innovations (‘Paludiculture’): enabling a paradigm shift towards the real-time sustainable production of ‘green-friendly’products and services. Science of the Total Environment, Amsterdam, v. 838, n. art. 156328, 2022. DOI: https://doi.org/10.1016/j.scitotenv.2022.156328

ROŽMAN, M.; OREŠKI, D.; TOMINC, P. A multidimensional model of the new work environment in the digital age to increase a company’s performance and competitiveness. IEEE Access, Piscataway, NJ, v. 11, p. 26136-26151, 2023. DOI: 10.1109/ACCESS.2023.3257104

RUSCH, M.; SCHÖGGL, J. P.; BAUMGARTNER, R. J. Application of digital technologies for sustainable product management in a circular economy: a review. Business Strategy and the Environment, Oxford, v. 32, n. 3, p. 1159-1174, 2023. DOI: https://doi.org/10.1002/bse.3099

RYAN, M. et al.An AI ethics ‘David and Goliath’: value conflicts between large tech companies and their employees. AI & Society, London, v. 39, n. 2, p. 557-572, 2024. DOI: https://doi.org/10.1007/s00146-022-01430-1

SALEEM, I. et al. The interplay of AI adoption, IoT edge, and adaptive resilience to explain digital innovation: evidence from German family-Owned SMEs. Journal of Theoretical and Applied Electronic Commerce Research, Basel, v. 18, n. 3, p. 1419-1430, 2023. DOI: https://doi.org/10.3390/jtaer18030071

SAMADHIYA, A. et al. Bridging realities into organizations through innovation and productivity: exploring the intersection of artificial intelligence, internet of things, and big data analytics in the metaverse environment using a multi-method approach. Decision Support Systems, Amsterdam, v. 185, n. art. 114290, 2024. DOI: https://doi.org/10.1016/j.dss.2024.114290

SEMTNER, A.; DZATOR, J.; NADOLNY, A. Is no (soft) skill left behind? Do soft skills enable job mobility. Applied Economics, Abingdon, v. 57, n. 33, p. 4897-4915, 2025. DOI: https://doi.org/10.1080/00036846.2024.2364103

SHAIK, T. et al. A review of the trends and challenges in adopting natural language processing methods for education feedback analysis. IEEE Access, Piscataway, NJ, v. 10, p. 56720-56739, 2022. DOI: 10.1109/ACCESS.2022.3177752

SINGH, R. K.; MODGIL, S.; SHORE, A. Building artificial intelligence enabled resilient supply chain: a multi-method approach. Journal of Enterprise Information Management, Bingley, v. 37, n. 2, p. 414-436, 2024. DOI: https://doi.org/10.1108/JEIM-09-2022-0326

SIPOLA, J.; SAUNILA, M.; UKKO, J. Adopting artificial intelligence in sustainable business. Journal of Cleaner Production, Amsterdam, v. 426, n. art. 139197, 2023. DOI: https://doi.org/10.1016/j.jclepro.2023.139197

SKLAVOS, G. et al. Environmental, social, and governance-based artificial intelligence governance: digitalizing firms’ leadership and human resources management. Sustainability, Basel, v. 16, n. 16, n. art. 7154, 2024. DOI: https://doi.org/10.3390/su16167154

SRIDHAR, A. et al. Digitalization of the agro-food sector for achieving sustainable development goals: a review. Sustainable Food Technology, Cambridge, v. 1, p. 783-802, 2023. Disponível em: https://doi.org/10.1039/D3FB00124E. Acesso em: 23 set. 2025.

SUÁREZ GIRI, F.; SÁNCHEZ CHAPARRO, T. Unveiling the blackbox within ESG ratings’ blackbox: toward a framework for analyzing AI adoption and its impacts. Business Strategy and Development, Oxford, v. 7, n. 4, n. art. e70038, 2024. DOI: https://doi.org/10.1002/bsd2.70038

SUTIENE, K. et al. Enhancing portfolio management using artificial intelligence: literature review. Frontiers in Artificial Intelligence, Lausanne, v. 7, n. art. 1371502, 2024. DOI: https://doi.org/10.3389/frai.2024.1371502

TAIROV, I. et al. Review of AI-driven solutions in business value and operational efficiency. Economics Ecology Socium, Odesa, v. 8, n. 3, p. 55-66, 2024. DOI: https://doi.org/10.61954/2616-7107/2024.8.3-5

TALUKDER, M. A. et al. Securing transactions: a hybrid dependable ensemble machine learning model using iht-lr and grid search. Cybersecurity, Singapore, v. 7, n. 1, n. art. 32, 2024. DOI: 10.1186/s42400-024-00221-z

TOBARRA, L. et al. A cloud game-based educative platform architecture: the cyberscratch project. Applied Sciences, Bucharest, v. 11, n. 2, n. art. 807, 2021. DOI: https://doi.org/10.3390/app11020807

TSANG, A.; FROST, T.; CAO, H. Environmental, social, and governance (ESG) disclosure: a literature review. The British Accounting Review, London, v. 55, n. 1, n. art. 101149, 2023. DOI: https://doi.org/10.1016/j.bar.2022.101149

UREN, V.; EDWARDS, J. S. Technology readiness and the organizational journey towards AI adoption: an empirical study. International Journal of Information Management, Oxford, v. 68, n. art. 102588, 2023. DOI: https://doi.org/10.1016/j.ijinfomgt.2022.102588

VĂRZARU, A. A. An empirical framework for assessment of the effects of digital technologies on sustainability accounting and reporting in the european union. Electronics, Basel, v. 11, n. 22, n. art. 3812, 2022. DOI: https://doi.org/10.3390/electronics11223812

VECCHIARINI, M.; SOMIÀ, T. Redefining entrepreneurship education in the age of artificial intelligence: an explorative analysis. The International Journal of Management Education, Oxford, v. 21, n. 3, n. art. 100879, 2023. DOI: https://doi.org/10.1016/j.ijme.2023.100879

VERGARA VILLEGAS, O. O. et al. Artificial intelligence for industry 4.0 in Iberoamerica. Computación y Sistemas, Buenos Aires, v. 25, n. 4, p. 761-773, 2021. DOI: https://doi.org/10.13053/cys-25-4-4056

WANG, J.; SUN, Q.; ZHOU, C. Insider threat detection based on deep clustering of Multi-Source behavioral events. Applied Sciences, Bucharest, v. 13, n. 24, n. art. 13021, 2023. DOI: https://doi.org/10.3390/app132413021

WANG, Y. et al. A literature review on the application of digital technology in achieving green supply chain management. Sustainability, Basel, v. 15, n. 11, n. art. 8564, 2023. DOI: https://doi.org/10.3390/su15118564

XIE, S. Improving explainability of major risk factors in artificial neural networks for auto insurance rate regulation. Risks, Basel, v. 9, n. 7, n. art. 126, 2021. DOI: https://doi.org/10.3390/risks9070126

XING, Y. et al. Uncovering the dark side of artificial intelligence in electronic markets: a systematic literature review. Journal of Organizational and end User Computing, Hershey, PA, v. 35, n. 1, p. 1-25, 2023. DOI: https://doi.org/10.4018/JOEUC.327278

XIONG, Y. The impact of artificial intelligence and digital economy consumer online shopping behavior on market changes. Discrete Dynamics in Nature and Society, Oxford, v. 2022, n. 1, n. art. 9772416, 2022. DOI: https://doi.org/10.1155/2022/9772416

YU, X.; XU, S.; ASHTON, M. Antecedents and outcomes of artificial intelligence adoption and application in the workplace: the socio-technical system theory perspective. Information Technology and People, Bingley v. 36, n. 1, p. 454-474, 2023. DOI: https://doi.org/10.1108/ITP-04-2021-0254

ZAID, M.; FAROOQI, R.; AZMI, S. N. Driving sustainable supply chain performance through digital transformation: the role of information exchange and responsiveness. Cogent Business & Management, Abingdon, v. 12, n. 1, n. art. 2443047, 2025. DOI: 10.1080/23311975.2024.2443047

ZAILANI, S. et al.Barriers to product return management in automotive manufacturing firms in Malaysia. Journal of Cleaner Production, Amsterdam, v. 141, p. 22-40, 2017. DOI: https://doi.org/10.1016/j.jclepro.2016.08.160

ZENG, X.; LI, S.; YOUSAF, Z. Artificial intelligence adoption and digital innovation: how does digital resilience act as a mediator and training protocols as a moderator? Sustainability, Basel, v. 14, n. 14, n. art. 8286, 2022. DOI: https://doi.org/10.3390/su14148286

ZHANG, H.; ZHENG, Z. [Retracted] Application and analysis of artificial intelligence in college students’ career planning and employment and entrepreneurship information recommendation. Security and Communication Networks, Hoboken, NJ, v. 2022, n. 1, n. art. 8073232, 2022. DOI: https://doi.org/10.1155/2022/8073232

ZHANG, Q.; ZHANG, J. A self-organized mapping neural network-based intelligent evaluation model for business capacity in enterprise management. IEEE Access, Piscataway, NJ, v. 11, p. 111801-111811, 2023. DOI: 10.1109/ACCESS.2023.3322320

ZHAO, Y. Decision support system for economic management of large enterprises based on artificial intelligence. Wireless Communications and Mobile Computing, Oxford, v. 2022, n. art. 9453580, 2022. DOI: https://doi.org/10.1155/2022/9453580

ZHU, C. Construction and risk analysis of marketing system based on AI. Scientific Programming, London, v. 2022, n. art. 2839834, 2022. DOI: https://doi.org/10.1155/2022/2839834

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